Biblio

Network security is a prominent topic that is gaining international attention. Distributed Denial of Service (DDoS) attack is often regarded as one of the most serious threats to network security. Software Defined Network (SDN) decouples the control plane from the data plane, which can meet various network requirements. But SDN can also become the object of DDoS attacks. This paper proposes an automated DDoS attack mitigation method that is based on the programmability of the Ryu controller and the features of the OpenFlow switch flow tables. The Mininet platform is used to simulate the whole process, from SDN traffic generation to using a K-Nearest Neighbor model for traffic classification, as well as identifying and mitigating DDoS attack. The packet counts of the victim's malicious traffic input port are significantly lower after the mitigation method is implemented than before the mitigation operation. The purpose of mitigating DDoS attack is successfully achieved.

Anomaly detection classification technology based on deep learning is one of the crucial technologies supporting network security. However, as the data increasing, this traditional model cannot guarantee that the false alarm rate is minimized while meeting the high detection rate. Additionally, distribution of imbalanced abnormal samples will lead to an increase in the error rate of the classification results. In this work, since CNN is effective in network intrusion classification, we embed a compressed feature layer in CNN (Convolutional Neural Networks). The purpose is to improve the efficiency of network intrusion detection. After our model was trained for 55 epochs and we set the learning rate of the model to 0.01, the detection rate reaches over 98%.